Delivering Business Intelligence and Data Analytics on Converged Systems

Business intelligence and big data analytics workloads place high demands on the underlying infrastructure in terms of performance, capacity, reliability and security. In addition, the enormous growth of unstructured data and the Internet of Things (IoT) forces IT departments to embrace infrastructure solutions that are both cost efficient and simple to deploy. If not, they run the risk of costs spiraling out of control to support these workloads.

According to Enterprise Strategy Group (ESG),1 IT decision-makers responsible for business intelligence, analytics and big data classified the following factors as the most important when choosing technology solutions for business intelligence and data analytics workloads:

Financial considerations

Security

Reliability

Performance

In addition to choosing the right database and analytics platforms, it is also essential to deploy an infrastructure that enables IT to achieve those goals. In the past, organizations often relied upon build-your-own (BYO) solutions for these very complex and demanding workloads. However, BYO turned out to be an extremely complicated and time-consuming challenge requiring cooperation and coordination across a wide range of disciplines, from database and analytics teams to compliance, security, compute, storage and networking.

Today, the emergence of converged solutions provides IT teams with a much less complex path to successful deployments—while also offering long-term benefits in reduced total cost of ownership (TCO). In addition, companies such as Dell EMC have developed converged solutions designed specifically to meet the needs of big data and analytics workloads, including systems that address the large-scale data storage needs of Hadoop data analytics. ESG makes a compelling case for using converged solutions for these workloads:

You can certainly build your own big data and IoT solutions from open source software and commodity hardware, assuming you have the time, talent and money. This approach may well be cheaper from a capital cost point of view, but it almost certainly will have significant hidden costs in staffing, support, and ongoing management of the environment. Big data and IoT as disciplines are too new, too complex, and too rapidly evolving to think that this will be easy either up front or over the years.

Instead, you can buy an engineered platform that’s ready to go. Plug it in, and focus on inventing and developing new business capabilities. You’ll have to evaluate whether your company’s core competency is in IT systems integration or in creating new competitive differentiation in your industry sector. For most, the latter is where they want their investments concentrated. The IT platform is merely a means to an end.2

A compelling use caseThe Annenberg School for Communication at the University of Pennsylvania provides a compelling example of how an organization can strategically leverage converged infrastructure to drive innovation in the areas of business intelligence and analytics.

Several years ago, the school recognized that it needed to glean insights from a broad new range of unstructured data sources such as Twitter, Facebook, YouTube and other social media feeds. The existing infrastructure couldn’t keep up with the big data generated by these networks, and the small IT staff struggled to keep the systems up and running. In response, the school replaced its previous infrastructure running on silos of servers, storage and networking with Dell EMC Vblock Systems to provide a private cloud environment.

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The converged infrastructure has enabled a big data platform that allows the school to work with much bigger data sets than ever before, along with a broad range of untraditional media sources. Users are able to leverage more data faster and more effectively for research, reporting, business analysis and myriad other uses. In addition, the converged infrastructure has been a boon to the IT organization, enabling:

Consolidation from two data centers to one.

A dramatic decrease in the time required for system maintenance tasks, such as firmware updates. Tasks that previously required five days to complete along with 12 hours of downtime are now done in one day with zero downtime.

A reduction in the time required for system health checks from 15 hours a week to two hours, leveraging automated and centralized system management.

Full deployment of the Dell EMC Vblock system into production within 60 days after the system was ordered.

ConclusionThe importance of business intelligence and big data analytics cannot be overstated in today’s environment. Organizations of all sizes in all industries are embracing digital transformation and seeking to leverage all varieties of data to achieve competitive business advantage. For IT teams, converged solutions provide a simple, fast and extremely cost-efficient path to building the infrastructure necessary to support these highly strategic workloads.